paul@torch.UUCP (paul) (11/04/86)
People who read the original posting in net.general (and the posting about neural networks in this newsgroup) may be interested in the following papers: Boltzmann Machines: Constraint Satisfaction Networks that Learn. by Geoffrey E. Hinton, Terrence J. Sejnowski and David H. Ackley Technical Report CMU-CS-84-119 (Carnegie-Mellon University May 1984) Optimisation by Simulated Annealing by S. Krikpatrick, C.D.Gelatt Jr., M.P.Vecchi Science Vol. 220 No. 4598 (13th May 1983). ...in addition to those recommended by Jonathan Marshall. Personally I regard this type of machine learning as something of a holy grail. In my opinion (and I stress that it IS my own opinion) this is THE way to get machines that are both massively parallel and capable of complex tasks without having a programmer who understands the in's and out's of the task to be accomplished and who is prepared to spend the time to hand code (or design) the machine necessary to do it. The only reservation I have is whether or not the basic theory behind Boltzmann machines is good enough. Paul.